feat(seaborn): implement line-annotated-events

[github-actions]

Implementation: line-annotated-events - seaborn

Implements the seaborn version of line-annotated-events.

File: plots/line-annotated-events/implementations/seaborn.py

Parent Issue: #2997

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a time series of daily sales (in units) from January 2024 to January 2025. The main line is rendered in Python blue (#306998) showing daily fluctuations with an upward trend from ~100 to ~180 units. Five yellow vertical dashed lines mark marketing events: Valentine's Campaign (Feb 14), Spring Sale (May 1), Summer Launch (Jul 15), Fall Promotion (Sep 20), and Black Friday (Nov 25). Each event has a yellow background label with bold text positioned at alternating heights (85%/75%) to avoid overlap. Yellow circular markers with dark edges appear on the line at each event date. The title uses the correct format "{spec-id} · seaborn · pyplots.ai". X-axis shows dates with rotated labels, Y-axis shows "Daily Sales (Units)" with clear tick marks. A subtle grid is visible in the background.

Quality Score: 92/100

Criteria Checklist

Visual Quality (36/40 pts)

• VQ-01: Text Legibility (10/10) - Title 24pt, axis labels 20pt, ticks 16pt - all perfectly readable
• VQ-02: No Overlap (8/8) - Event labels use alternating heights, no text overlap
• VQ-03: Element Visibility (8/8) - Line width 2.5 appropriate for daily data, markers s=150 visible
• VQ-04: Color Accessibility (5/5) - Blue line and yellow markers provide excellent contrast, colorblind-safe
• VQ-05: Layout Balance (5/5) - Plot fills canvas well, balanced margins with tight_layout
• VQ-06: Axis Labels (2/2) - "Daily Sales (Units)" with units, "Date" is descriptive
• VQ-07: Grid & Legend (0/2) - Grid is subtle at alpha=0.3 (good), but no legend explaining event marker colors

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct: line plot with event annotations
• SC-02: Data Mapping (5/5) - Date on X, sales on Y, events properly positioned
• SC-03: Required Features (5/5) - Vertical lines (axvline), markers on line, text labels - all present
• SC-04: Data Range (3/3) - All data visible, Y-axis range appropriate (95-185)
• SC-05: Legend Accuracy (2/2) - N/A (single series, events self-labeled)
• SC-06: Title Format (2/2) - "line-annotated-events · seaborn · pyplots.ai"

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (8/8) - Shows trend, seasonality, noise, and 5 diverse events across the year
• DQ-02: Realistic Context (5/7) - Product sales with marketing events is plausible, dates match real calendar events
• DQ-03: Appropriate Scale (5/5) - Sales values 95-185 units/day are realistic for e-commerce

Code Quality (10/10 pts)

• CQ-01: KISS Structure (3/3) - Clean: imports → data → plot → styling → save
• CQ-02: Reproducibility (3/3) - np.random.seed(42) used
• CQ-03: Clean Imports (2/2) - All imports used (plt, np, pd, sns)
• CQ-04: No Deprecated API (1/1) - Modern seaborn API
• CQ-05: Output Correct (1/1) - Saves as plot.png

Library Features (3/5 pts)

• LF-01: Uses distinctive library features (3/5) - Uses sns.lineplot correctly, but annotations done via matplotlib directly. Seaborn's statistical features not leveraged, though reasonable given the spec.

Strengths

• Excellent use of alternating label heights to prevent overlap with dense events
• Clear visual hierarchy: blue data line, yellow event markers create strong contrast
• Comprehensive data generation with realistic trend, seasonality, and noise
• Proper handling of event markers on the actual data line with scatter points
• Well-sized text elements for readability at high resolution

Weaknesses

• No legend or key explaining the event marker style (spec mentions "Include a subtle legend or key if multiple event types are shown" - though all events are same type here)
• Realistic context score could be higher with more domain-specific context (e.g., actual product category mentioned in labels)

Verdict: APPROVED